SAPO
A self-supervised reinforcement learning method that requires no manual temporal annotations. It uses the internal temporal attention of a VLM as a pseudo-label signal to drive GRPO optimization of video temporal grounding capability.
Timeline (last 90 days)
语义感知策略优化（SAPO）的核心是语义感知门控机制，该机制约束来自失败原子子任务的TD残差，阻止其跨任务边界传播。
AtomicRL的真实世界平均21.3%的提升依赖于Real2Sim2Real迁移流程的有效性，而仿真基准上的提升则主要来自SAPO和BATE对信用分配与探索的改进，两者贡献来源不同。
SAPO achieves a 33.7% relative mIoU gain on Qwen2.5-VL-3B.
SAPO achieves a 5.4% relative mIoU gain on Time-R1-7B.
SAPO (Self-supervised Attention Policy Optimization) uses the model's internal temporal attention as a self-supervised signal to drive Group Relative Policy Optimization (GRPO) to improve video temporal grounding.
The core idea of SAPO is to bypass reliance on manual annotation and optimize directly against the temporal generation gap, enabling the model to more accurately express information it already knows internally.
In the future, ideas similar to SAPO may be extended to other multimodal tasks that require precise output, such as spatial grounding or fine-grained action recognition.
SAPO's improvements transfer across datasets, and while improving grounding accuracy, the model's performance on general video understanding tasks does not degrade.
SAPO uses the attention interval as a pseudo-label to evaluate prediction quality and designs screening criteria that require no ground-truth annotations to pick out high-value training samples.
All Facts (9)
AtomicRL的真实世界平均21.3%的提升依赖于Real2Sim2Real迁移流程的有效性，而仿真基准上的提升则主要来自SAPO和BATE对信用分配与探索的改进，两者贡献来源不同。
50%Unverified语义感知策略优化（SAPO）的核心是语义感知门控机制，该机制约束来自失败原子子任务的TD残差，阻止其跨任务边界传播。
50%UnverifiedSAPO achieves a 33.7% relative mIoU gain on Qwen2.5-VL-3B.
50%UnverifiedSAPO achieves a 5.4% relative mIoU gain on Time-R1-7B.
50%UnverifiedSAPO (Self-supervised Attention Policy Optimization) uses the model's internal temporal attention as a self-supervised signal to drive Group Relative Policy Optimization (GRPO) to improve video temporal grounding.
50%UnverifiedThe core idea of SAPO is to bypass reliance on manual annotation and optimize directly against the temporal generation gap, enabling the model to more accurately express information it already knows internally.
50%UnverifiedSAPO's improvements transfer across datasets, and while improving grounding accuracy, the model's performance on general video understanding tasks does not degrade.
50%UnverifiedSAPO uses the attention interval as a pseudo-label to evaluate prediction quality and designs screening criteria that require no ground-truth annotations to pick out high-value training samples.
50%UnverifiedIn the future, ideas similar to SAPO may be extended to other multimodal tasks that require precise output, such as spatial grounding or fine-grained action recognition.
50%